scholarly journals Predictive Maintenance: An Autoencoder Anomaly-Based Approach for a 3 DoF Delta Robot

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 6979
Author(s):  
Kiavash Fathi ◽  
Hans Wernher van de Venn ◽  
Marcel Honegger
Keyword(s):  
Signal Sequence ◽  
Minimax Problem ◽  
Predictive Maintenance ◽  
Sigmoid Function ◽  
Degradation Model ◽  
Failure Data ◽  
Pick And Place ◽  
Place Task ◽  
Delta Robot ◽  
Useful Lifetime

Performing predictive maintenance (PdM) is challenging for many reasons. Dealing with large datasets which may not contain run-to-failure data (R2F) complicates PdM even more. When no R2F data are available, identifying condition indicators (CIs), estimating the health index (HI), and thereafter, calculating a degradation model for predicting the remaining useful lifetime (RUL) are merely impossible using supervised learning. In this paper, a 3 DoF delta robot used for pick and place task is studied. In the proposed method, autoencoders (AEs) are used to predict when maintenance is required based on the signal sequence distribution and anomaly detection, which is vital when no R2F data are available. Due to the sequential nature of the data, nonlinearity of the system, and correlations between parameter time-series, convolutional layers are used for feature extraction. Thereafter, a sigmoid function is used to predict the probability of having an anomaly given CIs acquired from AEs. This function can be manually tuned given the sensitivity of the system or optimized by solving a minimax problem. Moreover, the proposed architecture can be used for fault localization for the specified system. Additionally, the proposed method can calculate RUL using Gaussian process (GP), as a degradation model, given HI values as its input.

scholarly journals Predictive Maintenance: An Autoencoder Anomaly-based Approach for a 3 DoF Delta Robot

2021 ◽  
Author(s):  
Kiavash Fathi ◽  
Hans Wernher van de Venn ◽  
Marcel Honegger
Keyword(s):  
Signal Sequence ◽  
Minimax Problem ◽  
Predictive Maintenance ◽  
Sigmoid Function ◽  
Degradation Model ◽  
Failure Data ◽  
Pick And Place ◽  
Place Task ◽  
Delta Robot ◽  
Useful Lifetime

Performing predictive maintenance (PdM) is challenging for many reasons. Dealing with large datasets which may not contain run-to-failure data (R2F) complicates PdM even more. When no R2F data are available, identifying condition indicators (CIs), estimating the health index (HI), and thereafter, calculating a degradation model for predicting the remaining useful lifetime (RUL) are merely impossible using supervised learning. In this paper, a 3 dof delta robot used for pick and place task is studied. In the proposed method, autoencoders (AEs) are used to predict when maintenance is required based on the signal sequence distribution and anomaly detection, which is vital when no R2F data is available. Due to the sequential nature of the data, non-linearity of the system, and correlations between parameter time series, convolutional layers are used for feature extraction. Thereafter, a sigmoid function is used to predict the probability of having an anomaly given CIs acquired from AEs. This function can be manually tuned given the sensitivity of the system or optimized by solving a minimax problem. Moreover, the proposed architecture can be used for fault localization for the specified system. Additionally, the proposed method is capable of calculating RUL using Gaussian process (GP), as a degradation model, given HI values as its input.

scholarly journals Multimodal Mixed Reality Impact on a Hand Guiding Task with a Holographic Cobot

2020 ◽  
Vol 4 (4) ◽  
pp. 78
Author(s):  
Andoni Rivera Pinto ◽  
Johan Kildal ◽  
Elena Lazkano
Keyword(s):  
Augmented Reality ◽  
Industrial Production ◽  
User Study ◽  
Haptic Feedback ◽  
Mixed Reality ◽  
Robot Arm ◽  
Pick And Place ◽  
Place Task ◽  
Guidance Technique ◽  
The Impact

In the context of industrial production, a worker that wants to program a robot using the hand-guidance technique needs that the robot is available to be programmed and not in operation. This means that production with that robot is stopped during that time. A way around this constraint is to perform the same manual guidance steps on a holographic representation of the digital twin of the robot, using augmented reality technologies. However, this presents the limitation of a lack of tangibility of the visual holograms that the user tries to grab. We present an interface in which some of the tangibility is provided through ultrasound-based mid-air haptics actuation. We report a user study that evaluates the impact that the presence of such haptic feedback may have on a pick-and-place task of the wrist of a holographic robot arm which we found to be beneficial.

scholarly journals Queuing theory based part-flow estimation in a pick-and-place task with a multi-robot system

2018 ◽  
Vol 12 (2) ◽  
pp. JAMDSM0061-JAMDSM0061
Author(s):  
Yanjiang HUANG ◽  
Ryosuke CHIBA ◽  
Tamio ARAI ◽  
Tsuyoshi UEYAMA ◽  
Xianmin ZHANG ◽  
...  
Keyword(s):  
Queuing Theory ◽  
Robot System ◽  
Flow Estimation ◽  
Pick And Place ◽  
Place Task ◽  
Multi Robot

Development of Pick and Place Delta Robot

2020 ◽  
pp. 475-486
Author(s):  
Supod Kaewkorn ◽  
Chanin Joochim ◽  
Phongsak Keeratiwintakorn ◽  
Alisa Kunapinun
Keyword(s):  
Pick And Place ◽  
Delta Robot

scholarly journals Hidden Semi-Markov Models for Predictive Maintenance

2015 ◽  
Vol 2015 ◽  
pp. 1-23 ◽  
Author(s):  
Francesco Cartella ◽  
Jan Lemeire ◽  
Luca Dimiccoli ◽  
Hichem Sahli
Keyword(s):  
Markov Models ◽  
Real Data ◽  
Information Criterion ◽  
Absolute Error ◽  
Predictive Maintenance ◽  
Average Absolute Error ◽  
Current State ◽  
Automatic Model Selection ◽  
State Duration ◽  
Useful Lifetime

Realistic predictive maintenance approaches are essential for condition monitoring and predictive maintenance of industrial machines. In this work, we propose Hidden Semi-Markov Models (HSMMs) with (i) no constraints on the state duration density function and (ii) being applied to continuous or discrete observation. To deal with such a type of HSMM, we also propose modifications to the learning, inference, and prediction algorithms. Finally, automatic model selection has been made possible using the Akaike Information Criterion. This paper describes the theoretical formalization of the model as well as several experiments performed on simulated and real data with the aim of methodology validation. In all performed experiments, the model is able to correctly estimate the current state and to effectively predict the time to a predefined event with a low overall average absolute error. As a consequence, its applicability to real world settings can be beneficial, especially where in real time the Remaining Useful Lifetime (RUL) of the machine is calculated.

Analytical and Experimental Predictor-Based Time Delay Control of Baxter Robot

2018 ◽  
Author(s):  
Mostafa Bagheri ◽  
Miroslav Krstić ◽  
Peiman Naseradinmousavi
Keyword(s):  
Time Delay ◽  
Time Delays ◽  
Input Delay ◽  
Delay Systems ◽  
Engineering Systems ◽  
Common Control ◽  
Pick And Place ◽  
Place Task ◽  
Input Delays ◽  
Time Invariant

In this paper, a predictor-based controller for a 7-DOF Baxter manipulator is formulated to compensate a time-invariant input delay during a pick-and-place task. Robot manipulators are extensively employed because of their reliable, fast, and precise motions although they are subject to large time delays like many engineering systems. The time delay may lead to the lack of high precision required and even catastrophic instability. Using common control approaches on such delay systems can cause poor control performance, and uncompensated input delays can produce hazards when used in engineering applications. Therefore, destabilizing time delays need to be regarded in designing control law. First, delay-free dynamic equations are derived using the Lagrangian method. Then, we formulate a predictor-based controller for a 7-DOF Baxter manipulator, in the presence of input delay, in order to track desirable trajectories. Finally, the results are experimentally evaluated.

scholarly journals A Pick and Place Task with an KUKA Industrial Robot, Aided by an Arduino Board

2019 ◽  
Vol 6 (12) ◽  
pp. 398-400
Author(s):  
Rodrigo Barbosa Tudeschini ◽  
Raphael Barbosa Carneiro de Lima ◽  
Luiz Flavio Martins Pereira ◽  
Álvaro Manoel de Souza Soares
Keyword(s):  
Industrial Robot ◽  
Pick And Place ◽  
Place Task
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